(Submitted on November 1, 2017; Revised on December 10, 2017; Accepted on December 20, 2017)

Abstract:

In the process of user-based collaborative filtering algorithm, finding similar users effectively plays a crucial role in obtaining a high recommendation accuracy. The original rating matrix is very sparse, resulting in similarity information loss during similarity calculating and degrading the efficiency of similar users extracting. To tackle this problem, we propose an improved collaborative filtering algorithm based on Latent Semantic and Bayesian (ICFLSB). ICFLSB first utilizes Latent Semantic to extract meaningful features in the original rating matrix. Then, we establish a Bayesian model based on these extracted features to predict items which users have not rated but may be interested. Further, we fill the sparse original rating matrix with these predicted items and find similar users. After that, we adopt the collaborative filtering algorithm to conduct recommendations. Experiments show that ICFLSB proposed in this paper has a better recommendation performance than the traditional collaborative filtering algorithm. In particular, the evaluation results demonstrate that our ICFLSB can achieve a 2.152% higher and 1.152% higher on recommendation accuracy and recall rate respectively when compared to the traditional collaborative filtering algorithm.